Seamless and all time available positioning methods require novel systems and solutions that are specifically developed (and if necessary also deployed) for this purpose. The traditional positioning technologies, which are mainly used outdoors, i.e. satellite and cellular positioning technologies, cannot always deliver the desired performance that would enable seamless and equal navigation experience at all time, particularly indoors. As examples, required positioning accuracy (2-3 m), coverage (˜100%) and floor detection are challenging to achieve with satisfactory performance levels with the systems and signals that were not designed and specified for every use case in the first place. For instance, in case of indoor positioning, satellite-based radio navigation signals simply do not penetrate through the walls and roofs for the adequate signal reception and the cellular signals often have a too narrow bandwidth for accurate ranging by default. But also in outdoor scenarios, there may be the case of insufficient coverage of e.g. satellite-based radio navigation, for instance in case of bad weather or urban street canyons.
Several dedicated solutions have already been developed and commercially deployed during the past years e.g. solutions based on technologies like pseudolites (GPS-like short-range beacons), ultra-sound positioning, Bluetooth or Bluetooth LE signals and WLAN fingerprinting. What is typical to these solutions is that they require either deployment of totally new infrastructure (such as beacons or tags) or manual exhaustive radio-surveying of the streets and buildings including all the floors, spaces and rooms. This is rather expensive and will take a considerable amount of time to build the coverage to the commercially expected level, which can in some cases narrow the potential market segment to only a very thin customer base e.g. for health care or dedicated enterprise solutions. Also, the diversity of these technologies makes it difficult to build a globally scalable indoor positioning solution, and the integration and testing will become complex if a large number of technologies needs to be supported in the consumer devices, such as smartphones.
For an alternative positioning solution to be commercially successful it needs to be globally scalable, have low maintenance and deployment costs, and offer acceptable end-user experience. This can best be achieved, if the solution is based on an existing infrastructure in the buildings and on existing capabilities in the consumer devices. Accordingly, such a positioning is preferably based on technologies like Wi-Fi- and/or Bluetooth (BT)-technologies that are already supported in almost every smartphone, tablet, laptop and even in the majority of the feature phones. It is, thus, required to find a solution that uses such cellular or non-cellular radio signals in such a way that makes it possible to achieve 2-3 m horizontal and vertical positioning accuracy with the ability to quickly build the global coverage for this approach.
One approach for such radio-based positioning models e.g. the WLAN radio environment (or any similar radio e.g. Bluetooth) from observed Received Signal Strength (RSS)-measurements as (e.g. 2-dimensional) radio maps.
For this, accordingly high volumes of radio signal measurement data (so called radio fingerprints or simply fingerprints) need to be harvested via crowd-sourcing if the consumer devices are equipped with the necessary functionality to enable the radio signal data collection as a background process, naturally with the end-user consent. It could also be possible to use volunteers to survey the sites in exchange of reward or recognition and get the coverage climbing up globally in the places and venues important for the key customers. However, the technical challenges related to the harvesting, processing, redundancy, ambiguity and storing the crowd-sourced data need to be understood and solved first, before the radiomap creation can be based on the fully crowd-sourced data.
Specifically because crowd-sourcing is often a background process that does not directly benefit the device user, it is desirable that the crowd-sourcing process only consumes limited resources of the device. This is particularly problematic, because the described crowd-sourcing technique (even though very powerful) often produces close to 100% redundant data, i.e. the data is collected multiple times by the same device for the same location.
This can be understood by considering the typical daily routes people are travelling or places people are staying: from home to kindergarten to the work place back to the kindergarten and back home. Moreover, people stay most of the day at the workplace and nights at home, also part of the evening time.
Therefore, if crowdsourcing samples are taken e.g. periodically at fixed intervals, most of the data collected will be taken at same locations or along same routes over and over again. Thus, in the end it turns out that a large part (easily >90%) of the contributed data is just a repetition of already collected data.
This easily wastes resources not only on the client side, because of excessive using of the GNSS-based positioning, of WLAN scans and of data transmittal, but also on the server side, where typically all the incoming data needs to be processed.
Approaches of finding a balance between data collection and saving resources may comprise only collecting data, whenever some other application uses GNSS-based positioning, not taking samples during the night time or taking data samples at fixed time or spatial intervals (e.g. every 10 minutes, every 500 meters).
However, these approaches do often not provide optimal results regarding a saving of resources. Additionally, such approaches have the drawback that, depending on the approach, for certain scenarios or phases no data is collected at all, e.g. when no application uses the GNSS-based positioning or during the night time. This may in particular be problematic, because e.g. changes in the radio environment or infrastructure become hard to detect and, in the worst case, are not detected at all.